Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
One-class svms for document classification
The Journal of Machine Learning Research
ACM Computing Surveys (CSUR)
PRISM: platform for remote sensing using smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Proceedings of the 13th international conference on Ubiquitous computing
Anomaly Detection for Discrete Sequences: A Survey
IEEE Transactions on Knowledge and Data Engineering
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With the rise of smartphone platforms, adaptive sensing becomes an predominant key to overcome intricate constraints such as smartphone's capabilities and dynamic data. One way to do this is estimating the event probability based on anomaly detection to invoke heavy processes, such as switching on more sensors or retrieving information. However, most conventional anomaly detection methods are power hungry and computation consuming. This paper proposes a new online anomaly detection algorithm by capturing the likelihood of frequency histogram given features extracted from a stream of measurements from sensors of multiple smartphones. The algorithm then estimates the mixed density probability of anomalies. By doing so, the algorithm is lightweight and energy efficient, which underpins large scale mobile sensing applications. Experimental results run on Android phones are consistent with our theoretical analysis.